Sida Gao


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Noise Robust Named Entity Understanding for Voice Assistants
Deepak Muralidharan | Joel Ruben Antony Moniz | Sida Gao | Xiao Yang | Justine Kao | Stephen Pulman | Atish Kothari | Ray Shen | Yinying Pan | Vivek Kaul | Mubarak Seyed Ibrahim | Gang Xiang | Nan Dun | Yidan Zhou | Andy O | Yuan Zhang | Pooja Chitkara | Xuan Wang | Alkesh Patel | Kushal Tayal | Roger Zheng | Peter Grasch | Jason D Williams | Lin Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.


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Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors
Sida Gao | Matthew R. Gormley
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Most recent improvements in NLP come from changes to the neural network architectures modeling the text input. Yet, state-of-the-art models often rely on simple approaches to model the label space, e.g. bigram Conditional Random Fields (CRFs) in sequence tagging. More expressive graphical models are rarely used due to their prohibitive computational cost. In this work, we present an approach for efficiently training and decoding hybrids of graphical models and neural networks based on Gibbs sampling. Our approach is the natural adaptation of SampleRank (Wick et al., 2011) to neural models, and is widely applicable to tasks beyond sequence tagging. We apply our approach to named entity recognition and present a neural skip-chain CRF model, for which exact inference is impractical. The skip-chain model improves over a strong baseline on three languages from CoNLL-02/03. We obtain new state-of-the-art results on Dutch.